TY - JOUR
T1 - Optimization-based approaches for maximizing aggregate recommendation diversity
AU - Adomavicius, Gediminas
AU - Kwon, Youngok
PY - 2014
Y1 - 2014
N2 - Recommender systems are being used to help users find relevant items from a large set of alternatives in many online applications. Most existing recommendation techniques have focused on improving recommendation accuracy; however, diversity of recommendations has also been increasingly recognized in research literature as an important aspect of recommendation quality. This paper proposes several optimization-based approaches for improving aggregate diversity of top-N recommendations, including a greedy maximization heuristic, a graphtheoretic approach based on maximum flow or maximum bipartite matching computations, and an integer programming approach. The proposed approaches are evaluated using real-world movie rating data sets and demonstrate substantial improvements in both diversity and accuracy as compared to the recommendation reranking approaches, which have been introduced in prior literature for the purposes of diversity improvement and were used for baseline comparisons in our study. The paper also discusses the computational complexity and the scalability of the proposed approaches, as well as the potential directions for future work.
AB - Recommender systems are being used to help users find relevant items from a large set of alternatives in many online applications. Most existing recommendation techniques have focused on improving recommendation accuracy; however, diversity of recommendations has also been increasingly recognized in research literature as an important aspect of recommendation quality. This paper proposes several optimization-based approaches for improving aggregate diversity of top-N recommendations, including a greedy maximization heuristic, a graphtheoretic approach based on maximum flow or maximum bipartite matching computations, and an integer programming approach. The proposed approaches are evaluated using real-world movie rating data sets and demonstrate substantial improvements in both diversity and accuracy as compared to the recommendation reranking approaches, which have been introduced in prior literature for the purposes of diversity improvement and were used for baseline comparisons in our study. The paper also discusses the computational complexity and the scalability of the proposed approaches, as well as the potential directions for future work.
KW - Collaborative filtering
KW - Optimization techniques
KW - Recommendation accuracy
KW - Recommendation diversity
KW - Recommender systems
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U2 - 10.1287/ijoc.2013.0570
DO - 10.1287/ijoc.2013.0570
M3 - Article
AN - SCOPUS:84899474950
SN - 1091-9856
VL - 26
SP - 351
EP - 369
JO - INFORMS Journal on Computing
JF - INFORMS Journal on Computing
IS - 2
ER -